The invention generally relates to enhancement of video signals, and more particularly, to performing feature detection on an incoming video signal.
In a typical display system using digital processing of data, a video signal input is received at a signal interface. If the signal is not already digital, it is digitized by an analog-to-digital (A/D) conversion. A resulting digital signal is then sent to a processor, where it undergoes several possible processing steps, depending upon the type and architecture of the system. Possibilities include, but are not limited to: interlace to progressive scan (proscan) conversion (for interlaced signals); scaling; motion and edge detection interpolation; and color space conversion.
Some of these functions involve the application of low pass interpolation filters, such as proscan and scaling. For example, in typical broadcast signal display systems using NTSC television signals, the data is interlaced when displayed. Interlacing involves dividing a video frame of data into two fields: one with the odd-numbered lines of the field, the other with the even-numbered lines. This method came about because of the operation of the cathode ray tube (CRT), which makes two scans across a display screen per frame. A proscan operation is used to provide interlace-to-progressive-scan (proscan) conversion. Proscan conversion includes low pass filtering and therefore results in a lessening of the sharpness of an image.
Another process that results in decreased sharpness is scaling. Scaling is performed on images of one format to enlarge or shrink the image to fit another format. For example, standard 4:3 aspect ratio Letterbox NTSC must be scaled horizontally and vertically in order to fit on a 16:9 aspect ratio display. That is, the incoming data must be scaled to form data to be displayed in the other format. This processing also includes low pass interpolation operations and results in decreased sharpness.
Generally speaking, any operation having low pass interpolation filter properties results in a loss of detail in the image. Therefore, at some point in the processing of the data, sharpness control is typically added to the image.
FIG. 1 shows an example block diagram of a typical sharpness control unit 100. An incoming input image undergoes horizontal and vertical filtering by filters 102 and 104, respectively. The filtered image is then multiplied by a scaling factor that increases or decreases the magnitude of the filtered image, and the filtered image is recombined with the original image. The multiplied scaling factor is referred to as “gain,” and the process of multiplication at gain unit 106 is referred to as “applying gain.” The filtered image is also referred to as the “feature component.” As shown in FIG. 1, gain is added to the image at gain stage 106, and the filtered image with gain is then added back into the original image at adder 108. The sharpness adjustment results in an emphasis on the higher frequency edges in the image, with only higher frequency information being passed as an enhancement signal in both dimensions. This means that only point like objects and diagonal lines will be enhanced. While the resulting image will have some sharpness increase, it is not nearly as sharp as desired.
One problem with such an approach is that only objects that have both a horizontal and a vertical edge component will be acted upon by the sharpness process. Additionally, the implementation of FIG. 1 adds gain after the image data has undergone the low pass (or band pass) filter functions, resulting in an image that only has enhanced high frequency edges. Examples of high frequency horizontal and vertical edges components include diagonals and point objects. However, the filtering process does increase sharpness for data of those objects to which it does apply.
An additional problem with the typical sharpness control unit 100 shown in FIG. 1 is that “soft edges” are enhanced very little, and “hard edges” are enhanced a lot. The term soft “soft edge” refers to an edge that is visually not very pronounced. For example, a soft edge is an edge in a video picture that is blurry or is formed by a slight change in luminance value. The term “hard edge” refers to an edge that is visually very pronounced. For example, a hard edge is a very clear edge or a distinct edge formed by a large change in luminance value. The typical sharpness control unit 100 shown in FIG. 1 applies a gain using the gain unit 106. Because the filtered component corresponding to a soft edge is a very small value, even after applying the gain, soft edges are only minimally enhanced in the resulting image. Conversely, because the filtered component corresponding to a hard edge is a very large value, after applying the gain, hard edges are greatly enhanced in the resulting image. This is not an ideal situation because hard edges, by definition, were already clearly visible in the original image. It would be desirable to greatly increase the sharpness of soft images. However, if the gain of the gain unit 106 is increased in an attempt to sufficiently enhance soft edges, a well known problem of overshoot (and undershoot) occurs on the hard edges. Therefore, the typical sharpness control unit 100 can only provide a very limited sharpness enhancement on soft edges to avoid adding overshoot (and undershoot) effects to the image signal.